Type

Journal Article

Authors

Thomas Brendan Murphy
Michael Salter-Townshend

Subjects

Mathematics

Topics
family ego network finite mixture model expectation maximisation algorithm ego networks random role models maximum likelihood exponential random graph model networks mixtures expectation maximization machine learning statistics

Role Analysis in Networks using Mixtures of Exponential Random Graph Models. (2015)

Abstract A novel and flexible framework for investigating the roles of actors within a network is introduced. Particular interest is in roles as defined by local network connectivity patterns, identified using the ego-networks extracted from the network. A mixture of Exponential-family Random Graph Models is developed for these ego-networks in order to cluster the nodes into roles. We refer to this model as the ego-ERGM. An Expectation-Maximization algorithm is developed to infer the unobserved cluster assignments and to estimate the mixture model parameters using a maximum pseudo-likelihood approximation. The flexibility and utility of the method are demonstrated on examples of simulated and real networks.
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Ireland -> University College Dublin -> Mathematics and Statistics Research Collection
Ireland -> University College Dublin -> College of Science
Ireland -> University College Dublin -> College of Science
Ireland -> University College Dublin -> School of Mathematics and Statistics
Ireland -> University College Dublin -> Mathematics and Statistics Research Collection
Ireland -> University College Dublin -> School of Mathematics and Statistics
Ireland -> University College Dublin -> Institutes and Centres
Ireland -> University College Dublin -> Insight Centre for Data Analytics

Full list of authors on original publication

Thomas Brendan Murphy, Michael Salter-Townshend

Experts in our system

1
Thomas Brendan Murphy
University College Dublin
Total Publications: 38
 
2
Michael Salter-Townshend
University College Dublin
Total Publications: 12